Travelled to:1 × China
1 × Finland
1 × Italy
1 × Singapore
1 × Switzerland
1 × The Netherlands
4 × USA
Collaborated with:Y.Lu D.Schuurmans X.Wei B.Dumoulin N.Ahmed X.Li D.Metzler R.Jones R.Zhang R.Nallapati A.Feng J.Allan S.Wang Y.Zhao H.Tseng X.Huang N.Cercone S.E.Robertson
Talks about:search (4) queri (4) web (4) context (2) sensit (2) model (2) learn (2) text (2) discoveri (1) disambigu (1)
Person: Fuchun Peng
DBLP: Peng:Fuchun
Contributed to:
2010
200920092008200720062004
2003
20032002Wrote 10 papers:
- SIGIR-2010-LuPWD #personalisation #web
- Personalize web search results with user’s location (YL, FP, XW, BD), pp. 763–764.
- CIKM-2009-WeiPTLD #query #web
- Context sensitive synonym discovery for web search queries (XW, FP, HT, YL, BD), pp. 1585–1588.
- SIGIR-2009-MetzlerJPZ #query
- Improving search relevance for implicitly temporal queries (DM, RJ, FP, RZ), pp. 700–701.
- SIGIR-2008-WeiPD #query #web
- Analyzing web text association to disambiguate abbreviation in queries (XW, FP, BD), pp. 751–752.
- SIGIR-2007-PengALL #web
- Context sensitive stemming for web search (FP, NA, XL, YL), pp. 639–646.
- CIKM-2006-LuPLA #feature model #identification #machine learning #query
- Coupling feature selection and machine learning methods for navigational query identification (YL, FP, XL, NA), pp. 682–689.
- CIKM-2004-NallapatiFPA #thread #topic
- Event threading within news topics (RN, AF, FP, JA), pp. 446–453.
- ECIR-2003-PengS #classification #modelling #n-gram #naive bayes
- Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.
- ICML-2003-WangSPZ #learning #modelling #principle
- Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
- SIGIR-2002-PengHSCR #information retrieval #segmentation #self #using #word
- Using self-supervised word segmentation in Chinese information retrieval (FP, XH, DS, NC, SER), pp. 349–350.